package com.xwq.xwqaiagent.app.LoveApp;

import com.xwq.xwqaiagent.advisor.MyLoggerAdvisor;
import com.xwq.xwqaiagent.advisor.ReReadingAdvisor;
import com.xwq.xwqaiagent.advisor.SensitiveWordAdvisor;
import com.xwq.xwqaiagent.chatmemory.DatabaseChatMemory;
import com.xwq.xwqaiagent.rag.LoveAppRagCustomAdvisorFactory;
import com.xwq.xwqaiagent.rag.QueryRewriter;
import com.xwq.xwqaiagent.rag.QueryTranslation;
import jakarta.annotation.Resource;
import lombok.extern.slf4j.Slf4j;
import org.springframework.ai.chat.client.ChatClient;
import org.springframework.ai.chat.client.advisor.MessageChatMemoryAdvisor;
import org.springframework.ai.chat.client.advisor.QuestionAnswerAdvisor;
import org.springframework.ai.chat.client.advisor.api.Advisor;
import org.springframework.ai.chat.model.ChatModel;
import org.springframework.ai.chat.model.ChatResponse;
import org.springframework.ai.rag.retrieval.search.DocumentRetriever;
import org.springframework.ai.tool.ToolCallback;
import org.springframework.ai.vectorstore.VectorStore;
import org.springframework.stereotype.Component;

import java.util.List;

import static org.springframework.ai.chat.client.advisor.AbstractChatMemoryAdvisor.CHAT_MEMORY_CONVERSATION_ID_KEY;
import static org.springframework.ai.chat.client.advisor.AbstractChatMemoryAdvisor.CHAT_MEMORY_RETRIEVE_SIZE_KEY;

@Component
@Slf4j
public class LoveApp {

    private final ChatClient chatClient;


    private static final String SYSTEM_PROMPT = "扮演深耕恋爱心理领域的专家。开场向用户表明身份，告知用户可倾诉恋爱难题。" +
            "围绕单身、恋爱、已婚三种状态提问：单身状态询问社交圈拓展及追求心仪对象的困扰；" +
            "恋爱状态询问沟通、习惯差异引发的矛盾；已婚状态询问家庭责任与亲属关系处理的问题。" +
            "引导用户详述事情经过、对方反应及自身想法，以便给出专属解决方案。";

    /**
     * 初始化AI客户端
     * @param dashscopeChatModel
     */
    public LoveApp(ChatModel dashscopeChatModel,DatabaseChatMemory databaseChatMemory) {
        // 1.初始化基于文件的对话记忆
        String fileDir = System.getProperty("user.dir") + "/chat-memory";
        //ChatMemory chatMemory = new FileBasedChatMemory(fileDir);
        //2. 初始化基于内存的对话记忆
        //ChatMemory chatMemory = new InMemoryChatMemory();
        //3.初始化基于数据库的对话记忆
        //4.初始化基于redis的对话记忆

        chatClient = ChatClient.builder(dashscopeChatModel)
                .defaultSystem(SYSTEM_PROMPT)
                .defaultAdvisors(
                        new MessageChatMemoryAdvisor(databaseChatMemory),
                        //自定义日志Advisor，可以按需开启
                        new MyLoggerAdvisor(),
                        //自定义 Re2推理增强 Advisor，可以按需开启
                        new ReReadingAdvisor(),
                        new SensitiveWordAdvisor()
                )
                .build();
    }


    /**
     * AI基础对话（支持记忆多轮对话）
     * @param message
     * @param chatId
     * @return
     */
    public String doChat(String message, String chatId) {
        ChatResponse response = chatClient
                .prompt()
                .user(message)
                .advisors(spec -> spec.param(CHAT_MEMORY_CONVERSATION_ID_KEY, chatId)
                        .param(CHAT_MEMORY_RETRIEVE_SIZE_KEY, 20))
                .call()
                .chatResponse();
        String content = response.getResult().getOutput().getText();
        log.info("content: {}", content);
        return content;
    }

    record LoveReport(String title, List<String> suggestions) {
    }


    /**
     * AI恋爱报告（演示结构化输出）
     * @param message
     * @param chatId
     * @return
     */
    public LoveReport doChatWithReport(String message, String chatId) {
        LoveReport loveReport = chatClient
                .prompt()
                .system(SYSTEM_PROMPT + "每次对话后都要生成恋爱结果，标题为{用户名}的恋爱报告，内容为建议列表")
                .user(message)
                .advisors(spec -> spec.param(CHAT_MEMORY_CONVERSATION_ID_KEY, chatId)
                        .param(CHAT_MEMORY_RETRIEVE_SIZE_KEY, 20))
                .call()
                .entity(LoveReport.class);
        log.info("loveReport: {}", loveReport);
        return loveReport;
    }


    //ai恋爱大师基于知识库回答

    @Resource(name = "loveAppVectorStore")
    private VectorStore loveAppVectorStore;

    @Resource
    private Advisor loveAppRagCloudAdvisor;

    @Resource(name = "pgVectorVectorStore")
    private VectorStore pgVectorStore;

    @Resource
    private QueryRewriter queryRewriter;

    @Resource
    private QueryTranslation queryTranslation;

    @Resource
    private ToolCallback[] allTools;

    public String doChatWithRag(String message, String chatId) {

        //执行查询翻译（目标语言中文）
        String doQueryTranslation = queryTranslation.doQueryTranslation(message);

        //执行查询重写
        String queryRewrite = queryRewriter.doQueryRewrite(doQueryTranslation);

        ChatResponse chatResponse = chatClient
                .prompt()
                //.user(message)
                .user(queryRewrite)//这里使用查询重写后的提示词作为大模型输入
                .advisors(spec -> spec.param(CHAT_MEMORY_CONVERSATION_ID_KEY, chatId)
                        .param(CHAT_MEMORY_RETRIEVE_SIZE_KEY, 20))
                // 开启日志，便于观察效果
                .advisors(new MyLoggerAdvisor())
                // 应用知识库问答（基于内存）
                //.advisors(new QuestionAnswerAdvisor(loveAppVectorStore))
                // 应用增强检索服务（云知识库服务）
                //.advisors(loveAppRagCloudAdvisor)
                // 应用增强检索服务（Pgvecter）
                .advisors(new QuestionAnswerAdvisor(pgVectorStore))
                //应用自定义的RAG检索增强服务（文档检索器+上下文增强器）
                //.advisors(LoveAppRagCustomAdvisorFactory.createLoveAppRagCustomAdvisor(pgVectorStore,"单身"))
                .tools(allTools)
                .call()
                .chatResponse();
        String content = chatResponse.getResult().getOutput().getText();
        log.info("content: {}", content);
        return content;
    }

    public String doChatWithTools(String message, String chatId) {
        ChatResponse response = chatClient
                .prompt()
                .user(message)
                .advisors(spec -> spec.param(CHAT_MEMORY_CONVERSATION_ID_KEY, chatId)
                        .param(CHAT_MEMORY_RETRIEVE_SIZE_KEY, 10))
                // 开启日志，便于观察效果
                .advisors(new MyLoggerAdvisor())
                .tools(allTools)
                .call()
                .chatResponse();
        String content = response.getResult().getOutput().getText();
        log.info("content: {}", content);
        return content;
    }


}
